Developing a Python program for collecting stock data from CNN

• Main Author: Manickam, C.
• Other Authors: Wang Lipo
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Engineering
• Online Access: https://hdl.handle.net/10356/176924
• Institution: Nanyang Technological University

The stock market generates vast amounts of data, posing a challenge for investors and analysts to efficiently collect, analyze, and compare data from various sources. Traditional methods of data collection are time-consuming and tedious, hindering effective analysis and decision-making. To address this challenge, this project aims to develop a Python-based automated tool capable of extracting, analyzing, and comparing data from sources such as CNN, Yahoo Finance, and other stock prediction websites. This project focuses on the utilization of Python programming for gathering and analyzing stock data to determine intrinsic values using two distinct valuation methodologies: Discounted Cash Flow (DCF) analysis and the Graham Model. The research begins with data gathering through Python libraries, enabling the extraction of historical financial data including stock prices, earnings, dividends, and other pertinent metrics. Additionally, web scraping techniques are employed to gather supplementary data for comprehensive analysis. Subsequently, the study outlines the theoretical foundations and calculation methodologies of both the DCF and Graham Model approaches. The DCF method involves forecasting future cash flows and discounting them back to present value using an appropriate discount rate, whereas the Graham Model assesses intrinsic value based on earnings per share (EPS) and book value per share.